<!-- HTML header for doxygen 1.8.6-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta content="text/xhtml;charset=utf-8" http-equiv="Content-Type"/>
<meta content="IE=9" http-equiv="X-UA-Compatible"/>
<meta content="Doxygen 1.8.13" name="generator"/>
<title>OpenCV: cv::xfeatures2d::PCTSignatures Class Reference</title>
<link href="../../opencv.ico" rel="shortcut icon" type="image/x-icon"/>
<link href="../../tabs.css" rel="stylesheet" type="text/css"/>
<script src="../../jquery.js" type="text/javascript"></script>
<script src="../../dynsections.js" type="text/javascript"></script>
<script src="../../tutorial-utils.js" type="text/javascript"></script>
<link href="../../search/search.css" rel="stylesheet" type="text/css"/>
<script src="../../search/searchdata.js" type="text/javascript"></script>
<script src="../../search/search.js" type="text/javascript"></script>
<script type="text/x-mathjax-config">
  MathJax.Hub.Config({
    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
    jax: ["input/TeX","output/HTML-CSS"],
});
//<![CDATA[
MathJax.Hub.Config(
{
  TeX: {
      Macros: {
          matTT: [ "\\[ \\left|\\begin{array}{ccc} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \\end{array}\\right| \\]", 9],
          fork: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ \\end{array} \\right.", 4],
          forkthree: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ #5 & \\mbox{#6}\\\\ \\end{array} \\right.", 6],
          forkfour: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ #5 & \\mbox{#6}\\\\ #7 & \\mbox{#8}\\\\ \\end{array} \\right.", 8],
          vecthree: ["\\begin{bmatrix} #1\\\\ #2\\\\ #3 \\end{bmatrix}", 3],
          vecthreethree: ["\\begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \\end{bmatrix}", 9],
          cameramatrix: ["#1 = \\begin{bmatrix} f_x & 0 & c_x\\\\ 0 & f_y & c_y\\\\ 0 & 0 & 1 \\end{bmatrix}", 1],
          distcoeffs: ["(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \\tau_x, \\tau_y]]]]) \\text{ of 4, 5, 8, 12 or 14 elements}"],
          distcoeffsfisheye: ["(k_1, k_2, k_3, k_4)"],
          hdotsfor: ["\\dots", 1],
          mathbbm: ["\\mathbb{#1}", 1],
          bordermatrix: ["\\matrix{#1}", 1]
      }
  }
}
);
//]]>
</script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" type="text/javascript"></script>
<link href="../../doxygen.css" rel="stylesheet" type="text/css"/>
<link href="../../stylesheet.css" rel="stylesheet" type="text/css"/>
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<!--#include virtual="/google-search.html"-->
<table cellpadding="0" cellspacing="0">
 <tbody>
 <tr style="height: 56px;">
  <td id="projectlogo"><img alt="Logo" src="../../opencv-logo-small.png"/></td>
  <td style="padding-left: 0.5em;">
   <div id="projectname">OpenCV
    <span id="projectnumber">4.5.2</span>
   </div>
   <div id="projectbrief">Open Source Computer Vision</div>
  </td>
 </tr>
 </tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.13 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "../../search",false,'Search');
</script>
<script src="../../menudata.js" type="text/javascript"></script>
<script src="../../menu.js" type="text/javascript"></script>
<script type="text/javascript">
$(function() {
  initMenu('../../',true,false,'search.php','Search');
  $(document).ready(function() { init_search(); });
});
</script>
<div id="main-nav"></div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow" onkeydown="return searchBox.OnSearchSelectKey(event)" onmouseout="return searchBox.OnSearchSelectHide()" onmouseover="return searchBox.OnSearchSelectShow()">
</div>
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
<iframe frameborder="0" id="MSearchResults" name="MSearchResults" src="javascript:void(0)">
</iframe>
</div>
<div class="navpath" id="nav-path">
  <ul>
<li class="navelem"><a class="el" href="../../d2/d75/namespacecv.html">cv</a></li><li class="navelem"><a class="el" href="../../d3/df6/namespacecv_1_1xfeatures2d.html">xfeatures2d</a></li><li class="navelem"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html">PCTSignatures</a></li>  </ul>
</div>
</div><!-- top -->
<div class="header">
  <div class="summary">
<a href="#pub-types">Public Types</a> |
<a href="#pub-methods">Public Member Functions</a> |
<a href="#pub-static-methods">Static Public Member Functions</a> |
<a href="../../d6/d15/classcv_1_1xfeatures2d_1_1PCTSignatures-members.html">List of all members</a>  </div>
  <div class="headertitle">
<div class="title">cv::xfeatures2d::PCTSignatures Class Reference<span class="mlabels"><span class="mlabel">abstract</span></span><div class="ingroups"><a class="el" href="../../d1/db4/group__xfeatures2d.html">Extra 2D Features Framework</a> » <a class="el" href="../../d7/d7a/group__xfeatures2d__experiment.html">Experimental 2D Features Algorithms</a></div></div>  </div>
</div><!--header-->
<div class="contents">
<p>Class implementing PCT (position-color-texture) signature extraction as described in <a class="el" href="../../d0/de3/citelist.html#CITEREF_KrulisLS16">[133]</a>. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image.  
 <a href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#details">More...</a></p>
<p><code>#include &lt;opencv2/xfeatures2d.hpp&gt;</code></p>
<div class="dynheader">
Inheritance diagram for cv::xfeatures2d::PCTSignatures:</div>
<div class="dyncontent">
 <div class="center">
  <img alt="" src="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.png" usemap="#cv::xfeatures2d::PCTSignatures_map"/>
  <map id="cv::xfeatures2d::PCTSignatures_map" name="cv::xfeatures2d::PCTSignatures_map">
<area alt="cv::Algorithm" coords="0,0,188,24" href="../../d3/d46/classcv_1_1Algorithm.html" shape="rect" title="This is a base class for all more or less complex algorithms in OpenCV. "/>
</map>
 </div></div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:afbfb1e721fa42ab53fe6cd733a51af9c"><td align="right" class="memItemLeft" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9c">DistanceFunction</a> { <br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9ca329bc6c9f8b2f66e0074b5b7d53c6c44">L0_25</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9ca6f767af8fdecce184366a58e6446f0f6">L0_5</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9caa4d7e3bd3f4f28c1a9460b5b0314395a">L1</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9cac9cff6959282d25cd4c9cc3008a17dcc">L2</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9ca512d1f81e9e6f513bf7c338947a430cd">L2SQUARED</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9ca9b61ece212bb491a39009290c1b47b9b">L5</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9ca66115018ae41e0ef9a0bacd83fbbccfc">L_INFINITY</a>
<br/>
 }<tr class="memdesc:afbfb1e721fa42ab53fe6cd733a51af9c"><td class="mdescLeft"> </td><td class="mdescRight">Lp distance function selector.  <a href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9c">More...</a><br/></td></tr>
</td></tr>
<tr class="separator:afbfb1e721fa42ab53fe6cd733a51af9c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa2a27f17a1a30fad4c54c248b77777e0"><td align="right" class="memItemLeft" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa2a27f17a1a30fad4c54c248b77777e0">PointDistribution</a> { <br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa2a27f17a1a30fad4c54c248b77777e0a5fb71e96bee38313ae3600a6d8204248">UNIFORM</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa2a27f17a1a30fad4c54c248b77777e0a9904ebe58740486ab511151a7b9067fd">REGULAR</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa2a27f17a1a30fad4c54c248b77777e0af115eca6e3361cd5ec6cb1887f0aabd3">NORMAL</a>
<br/>
 }<tr class="memdesc:aa2a27f17a1a30fad4c54c248b77777e0"><td class="mdescLeft"> </td><td class="mdescRight">Point distributions supported by random point generator.  <a href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa2a27f17a1a30fad4c54c248b77777e0">More...</a><br/></td></tr>
</td></tr>
<tr class="separator:aa2a27f17a1a30fad4c54c248b77777e0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac72268153bf12925f601c4defe7d5e50"><td align="right" class="memItemLeft" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ac72268153bf12925f601c4defe7d5e50">SimilarityFunction</a> { <br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ac72268153bf12925f601c4defe7d5e50ae4c8b209454fa9578ddb3af45da854c2">MINUS</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ac72268153bf12925f601c4defe7d5e50a90fc80ce009b279cde69707d90988268">GAUSSIAN</a>, 
<br/>
  <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ac72268153bf12925f601c4defe7d5e50a999778061578f114ebf7ec41b3f5d858">HEURISTIC</a>
<br/>
 }<tr class="memdesc:ac72268153bf12925f601c4defe7d5e50"><td class="mdescLeft"> </td><td class="mdescRight">Similarity function selector.  <a href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ac72268153bf12925f601c4defe7d5e50">More...</a><br/></td></tr>
</td></tr>
<tr class="separator:ac72268153bf12925f601c4defe7d5e50"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a23afba0a61f447c839fbf0134af817eb"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a23afba0a61f447c839fbf0134af817eb">computeSignature</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> image, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> signature) const =0</td></tr>
<tr class="memdesc:a23afba0a61f447c839fbf0134af817eb"><td class="mdescLeft"> </td><td class="mdescRight">Computes signature of given image.  <a href="#a23afba0a61f447c839fbf0134af817eb">More...</a><br/></td></tr>
<tr class="separator:a23afba0a61f447c839fbf0134af817eb"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7e64cf57c74009277db78e99d14af4d2"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a7e64cf57c74009277db78e99d14af4d2">computeSignatures</a> (const std::vector&lt; <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &gt; &amp;images, std::vector&lt; <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &gt; &amp;signatures) const =0</td></tr>
<tr class="memdesc:a7e64cf57c74009277db78e99d14af4d2"><td class="mdescLeft"> </td><td class="mdescRight">Computes signatures for multiple images in parallel.  <a href="#a7e64cf57c74009277db78e99d14af4d2">More...</a><br/></td></tr>
<tr class="separator:a7e64cf57c74009277db78e99d14af4d2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a80b1bcbb2621d01887f0ce0cf2153d27"><td align="right" class="memItemLeft" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a80b1bcbb2621d01887f0ce0cf2153d27">getClusterMinSize</a> () const =0</td></tr>
<tr class="memdesc:a80b1bcbb2621d01887f0ce0cf2153d27"><td class="mdescLeft"> </td><td class="mdescRight">This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.  <a href="#a80b1bcbb2621d01887f0ce0cf2153d27">More...</a><br/></td></tr>
<tr class="separator:a80b1bcbb2621d01887f0ce0cf2153d27"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a00e5d015965077e63a3d026315d99fb7"><td align="right" class="memItemLeft" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a00e5d015965077e63a3d026315d99fb7">getDistanceFunction</a> () const =0</td></tr>
<tr class="memdesc:a00e5d015965077e63a3d026315d99fb7"><td class="mdescLeft"> </td><td class="mdescRight">Distance function selector used for measuring distance between two points in k-means.  <a href="#a00e5d015965077e63a3d026315d99fb7">More...</a><br/></td></tr>
<tr class="separator:a00e5d015965077e63a3d026315d99fb7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa0de847070a8f7557feed32220f6d097"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa0de847070a8f7557feed32220f6d097">getDropThreshold</a> () const =0</td></tr>
<tr class="memdesc:aa0de847070a8f7557feed32220f6d097"><td class="mdescLeft"> </td><td class="mdescRight">Remove centroids in k-means whose weight is lesser or equal to given threshold.  <a href="#aa0de847070a8f7557feed32220f6d097">More...</a><br/></td></tr>
<tr class="separator:aa0de847070a8f7557feed32220f6d097"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a147d4d9af6c8f0fac8509a4f62d20306"><td align="right" class="memItemLeft" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a147d4d9af6c8f0fac8509a4f62d20306">getGrayscaleBits</a> () const =0</td></tr>
<tr class="memdesc:a147d4d9af6c8f0fac8509a4f62d20306"><td class="mdescLeft"> </td><td class="mdescRight">Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.  <a href="#a147d4d9af6c8f0fac8509a4f62d20306">More...</a><br/></td></tr>
<tr class="separator:a147d4d9af6c8f0fac8509a4f62d20306"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2d73ca7a104084525f33f76b8f062da7"><td align="right" class="memItemLeft" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a2d73ca7a104084525f33f76b8f062da7">getInitSeedCount</a> () const =0</td></tr>
<tr class="memdesc:a2d73ca7a104084525f33f76b8f062da7"><td class="mdescLeft"> </td><td class="mdescRight">Number of initial seeds (initial number of clusters) for the k-means algorithm.  <a href="#a2d73ca7a104084525f33f76b8f062da7">More...</a><br/></td></tr>
<tr class="separator:a2d73ca7a104084525f33f76b8f062da7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a05099a75bbbe848a83aba350686ce20d"><td align="right" class="memItemLeft" valign="top">virtual std::vector&lt; int &gt; </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a05099a75bbbe848a83aba350686ce20d">getInitSeedIndexes</a> () const =0</td></tr>
<tr class="memdesc:a05099a75bbbe848a83aba350686ce20d"><td class="mdescLeft"> </td><td class="mdescRight">Initial seeds (initial number of clusters) for the k-means algorithm.  <a href="#a05099a75bbbe848a83aba350686ce20d">More...</a><br/></td></tr>
<tr class="separator:a05099a75bbbe848a83aba350686ce20d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa09a2face423a7042bdec81e6526190b"><td align="right" class="memItemLeft" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa09a2face423a7042bdec81e6526190b">getIterationCount</a> () const =0</td></tr>
<tr class="memdesc:aa09a2face423a7042bdec81e6526190b"><td class="mdescLeft"> </td><td class="mdescRight">Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).  <a href="#aa09a2face423a7042bdec81e6526190b">More...</a><br/></td></tr>
<tr class="separator:aa09a2face423a7042bdec81e6526190b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab140533fa85a158ebbbed43d9e03973b"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ab140533fa85a158ebbbed43d9e03973b">getJoiningDistance</a> () const =0</td></tr>
<tr class="memdesc:ab140533fa85a158ebbbed43d9e03973b"><td class="mdescLeft"> </td><td class="mdescRight">Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.  <a href="#ab140533fa85a158ebbbed43d9e03973b">More...</a><br/></td></tr>
<tr class="separator:ab140533fa85a158ebbbed43d9e03973b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a68d214927159651492e648e5adde1345"><td align="right" class="memItemLeft" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a68d214927159651492e648e5adde1345">getMaxClustersCount</a> () const =0</td></tr>
<tr class="memdesc:a68d214927159651492e648e5adde1345"><td class="mdescLeft"> </td><td class="mdescRight">Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.  <a href="#a68d214927159651492e648e5adde1345">More...</a><br/></td></tr>
<tr class="separator:a68d214927159651492e648e5adde1345"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a16c500a45db6acdf2d5989d2f0563bfc"><td align="right" class="memItemLeft" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a16c500a45db6acdf2d5989d2f0563bfc">getSampleCount</a> () const =0</td></tr>
<tr class="memdesc:a16c500a45db6acdf2d5989d2f0563bfc"><td class="mdescLeft"> </td><td class="mdescRight">Number of initial samples taken from the image.  <a href="#a16c500a45db6acdf2d5989d2f0563bfc">More...</a><br/></td></tr>
<tr class="separator:a16c500a45db6acdf2d5989d2f0563bfc"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3587514c31b90a137b87b437f373cbdd"><td align="right" class="memItemLeft" valign="top">virtual std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a3587514c31b90a137b87b437f373cbdd">getSamplingPoints</a> () const =0</td></tr>
<tr class="memdesc:a3587514c31b90a137b87b437f373cbdd"><td class="mdescLeft"> </td><td class="mdescRight">Initial samples taken from the image. These sampled features become the input for clustering.  <a href="#a3587514c31b90a137b87b437f373cbdd">More...</a><br/></td></tr>
<tr class="separator:a3587514c31b90a137b87b437f373cbdd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a92ea655d80cc73ebc4d9c4528a2e13aa"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a92ea655d80cc73ebc4d9c4528a2e13aa">getWeightA</a> () const =0</td></tr>
<tr class="memdesc:a92ea655d80cc73ebc4d9c4528a2e13aa"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#a92ea655d80cc73ebc4d9c4528a2e13aa">More...</a><br/></td></tr>
<tr class="separator:a92ea655d80cc73ebc4d9c4528a2e13aa"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2c05236f9cb324ea0b457cc4aad73002"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a2c05236f9cb324ea0b457cc4aad73002">getWeightB</a> () const =0</td></tr>
<tr class="memdesc:a2c05236f9cb324ea0b457cc4aad73002"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#a2c05236f9cb324ea0b457cc4aad73002">More...</a><br/></td></tr>
<tr class="separator:a2c05236f9cb324ea0b457cc4aad73002"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8ba46a2ee95336bd50ce38c5f7bfe895"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a8ba46a2ee95336bd50ce38c5f7bfe895">getWeightContrast</a> () const =0</td></tr>
<tr class="memdesc:a8ba46a2ee95336bd50ce38c5f7bfe895"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#a8ba46a2ee95336bd50ce38c5f7bfe895">More...</a><br/></td></tr>
<tr class="separator:a8ba46a2ee95336bd50ce38c5f7bfe895"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2c13123bc1ca583006de657ab640e921"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a2c13123bc1ca583006de657ab640e921">getWeightEntropy</a> () const =0</td></tr>
<tr class="memdesc:a2c13123bc1ca583006de657ab640e921"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#a2c13123bc1ca583006de657ab640e921">More...</a><br/></td></tr>
<tr class="separator:a2c13123bc1ca583006de657ab640e921"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a66b254681f41fe131ce9f9355de977ff"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a66b254681f41fe131ce9f9355de977ff">getWeightL</a> () const =0</td></tr>
<tr class="memdesc:a66b254681f41fe131ce9f9355de977ff"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#a66b254681f41fe131ce9f9355de977ff">More...</a><br/></td></tr>
<tr class="separator:a66b254681f41fe131ce9f9355de977ff"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1b3c726c6d7485a9f20ab9ecdecb79a1"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a1b3c726c6d7485a9f20ab9ecdecb79a1">getWeightX</a> () const =0</td></tr>
<tr class="memdesc:a1b3c726c6d7485a9f20ab9ecdecb79a1"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#a1b3c726c6d7485a9f20ab9ecdecb79a1">More...</a><br/></td></tr>
<tr class="separator:a1b3c726c6d7485a9f20ab9ecdecb79a1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:afca4d096cd24693c007c8197f2242591"><td align="right" class="memItemLeft" valign="top">virtual float </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afca4d096cd24693c007c8197f2242591">getWeightY</a> () const =0</td></tr>
<tr class="memdesc:afca4d096cd24693c007c8197f2242591"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#afca4d096cd24693c007c8197f2242591">More...</a><br/></td></tr>
<tr class="separator:afca4d096cd24693c007c8197f2242591"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae382e1d59ca2eaf13c482c6cffc50749"><td align="right" class="memItemLeft" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ae382e1d59ca2eaf13c482c6cffc50749">getWindowRadius</a> () const =0</td></tr>
<tr class="memdesc:ae382e1d59ca2eaf13c482c6cffc50749"><td class="mdescLeft"> </td><td class="mdescRight">Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).  <a href="#ae382e1d59ca2eaf13c482c6cffc50749">More...</a><br/></td></tr>
<tr class="separator:ae382e1d59ca2eaf13c482c6cffc50749"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9b1788e47f815b0bdba559062e1be914"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a9b1788e47f815b0bdba559062e1be914">setClusterMinSize</a> (int clusterMinSize)=0</td></tr>
<tr class="memdesc:a9b1788e47f815b0bdba559062e1be914"><td class="mdescLeft"> </td><td class="mdescRight">This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.  <a href="#a9b1788e47f815b0bdba559062e1be914">More...</a><br/></td></tr>
<tr class="separator:a9b1788e47f815b0bdba559062e1be914"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a828b6802c9960647a128b966f7a79045"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a828b6802c9960647a128b966f7a79045">setDistanceFunction</a> (int distanceFunction)=0</td></tr>
<tr class="memdesc:a828b6802c9960647a128b966f7a79045"><td class="mdescLeft"> </td><td class="mdescRight">Distance function selector used for measuring distance between two points in k-means. Available: L0_25, L0_5, <a class="el" href="../../d4/d7f/structcv_1_1L1.html">L1</a>, <a class="el" href="../../dc/daa/structcv_1_1L2.html">L2</a>, L2SQUARED, L5, L_INFINITY.  <a href="#a828b6802c9960647a128b966f7a79045">More...</a><br/></td></tr>
<tr class="separator:a828b6802c9960647a128b966f7a79045"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a48a8b9dcd84024b8cbdcd6d9eae220b7"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a48a8b9dcd84024b8cbdcd6d9eae220b7">setDropThreshold</a> (float dropThreshold)=0</td></tr>
<tr class="memdesc:a48a8b9dcd84024b8cbdcd6d9eae220b7"><td class="mdescLeft"> </td><td class="mdescRight">Remove centroids in k-means whose weight is lesser or equal to given threshold.  <a href="#a48a8b9dcd84024b8cbdcd6d9eae220b7">More...</a><br/></td></tr>
<tr class="separator:a48a8b9dcd84024b8cbdcd6d9eae220b7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1d9a4dd5b5b91d8e9f8195c7e496b9c3"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a1d9a4dd5b5b91d8e9f8195c7e496b9c3">setGrayscaleBits</a> (int grayscaleBits)=0</td></tr>
<tr class="memdesc:a1d9a4dd5b5b91d8e9f8195c7e496b9c3"><td class="mdescLeft"> </td><td class="mdescRight">Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.  <a href="#a1d9a4dd5b5b91d8e9f8195c7e496b9c3">More...</a><br/></td></tr>
<tr class="separator:a1d9a4dd5b5b91d8e9f8195c7e496b9c3"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0aa3ed847a3bd993e34a9bdb294212f5"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a0aa3ed847a3bd993e34a9bdb294212f5">setInitSeedIndexes</a> (std::vector&lt; int &gt; initSeedIndexes)=0</td></tr>
<tr class="memdesc:a0aa3ed847a3bd993e34a9bdb294212f5"><td class="mdescLeft"> </td><td class="mdescRight">Initial seed indexes for the k-means algorithm.  <a href="#a0aa3ed847a3bd993e34a9bdb294212f5">More...</a><br/></td></tr>
<tr class="separator:a0aa3ed847a3bd993e34a9bdb294212f5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa2f1491f6cc4f99feed74a55c84a6950"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa2f1491f6cc4f99feed74a55c84a6950">setIterationCount</a> (int iterationCount)=0</td></tr>
<tr class="memdesc:aa2f1491f6cc4f99feed74a55c84a6950"><td class="mdescLeft"> </td><td class="mdescRight">Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).  <a href="#aa2f1491f6cc4f99feed74a55c84a6950">More...</a><br/></td></tr>
<tr class="separator:aa2f1491f6cc4f99feed74a55c84a6950"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3b34e63ce62c9759d7a9747ba6549a78"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a3b34e63ce62c9759d7a9747ba6549a78">setJoiningDistance</a> (float joiningDistance)=0</td></tr>
<tr class="memdesc:a3b34e63ce62c9759d7a9747ba6549a78"><td class="mdescLeft"> </td><td class="mdescRight">Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.  <a href="#a3b34e63ce62c9759d7a9747ba6549a78">More...</a><br/></td></tr>
<tr class="separator:a3b34e63ce62c9759d7a9747ba6549a78"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4450e6f3e2676caaf4c78c9da7e6af6f"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a4450e6f3e2676caaf4c78c9da7e6af6f">setMaxClustersCount</a> (int maxClustersCount)=0</td></tr>
<tr class="memdesc:a4450e6f3e2676caaf4c78c9da7e6af6f"><td class="mdescLeft"> </td><td class="mdescRight">Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.  <a href="#a4450e6f3e2676caaf4c78c9da7e6af6f">More...</a><br/></td></tr>
<tr class="separator:a4450e6f3e2676caaf4c78c9da7e6af6f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a04adf50e23489c41975a6b7524c189d4"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a04adf50e23489c41975a6b7524c189d4">setSamplingPoints</a> (std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; samplingPoints)=0</td></tr>
<tr class="memdesc:a04adf50e23489c41975a6b7524c189d4"><td class="mdescLeft"> </td><td class="mdescRight">Sets sampling points used to sample the input image.  <a href="#a04adf50e23489c41975a6b7524c189d4">More...</a><br/></td></tr>
<tr class="separator:a04adf50e23489c41975a6b7524c189d4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a326cff067288459c6fefa0f3fe313a14"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a326cff067288459c6fefa0f3fe313a14">setTranslation</a> (int idx, float value)=0</td></tr>
<tr class="memdesc:a326cff067288459c6fefa0f3fe313a14"><td class="mdescLeft"> </td><td class="mdescRight">Translations of the individual axes of the feature space.  <a href="#a326cff067288459c6fefa0f3fe313a14">More...</a><br/></td></tr>
<tr class="separator:a326cff067288459c6fefa0f3fe313a14"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a48a29104ac014ba9adc426ec67568ed7"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a48a29104ac014ba9adc426ec67568ed7">setTranslations</a> (const std::vector&lt; float &gt; &amp;translations)=0</td></tr>
<tr class="memdesc:a48a29104ac014ba9adc426ec67568ed7"><td class="mdescLeft"> </td><td class="mdescRight">Translations of the individual axes of the feature space.  <a href="#a48a29104ac014ba9adc426ec67568ed7">More...</a><br/></td></tr>
<tr class="separator:a48a29104ac014ba9adc426ec67568ed7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a644513e799afdefdb0e37a1bb4abc2d7"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a644513e799afdefdb0e37a1bb4abc2d7">setWeight</a> (int idx, float value)=0</td></tr>
<tr class="memdesc:a644513e799afdefdb0e37a1bb4abc2d7"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space.  <a href="#a644513e799afdefdb0e37a1bb4abc2d7">More...</a><br/></td></tr>
<tr class="separator:a644513e799afdefdb0e37a1bb4abc2d7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:adb1cdee9bc21e1486795970ad4f5226f"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#adb1cdee9bc21e1486795970ad4f5226f">setWeightA</a> (float weight)=0</td></tr>
<tr class="memdesc:adb1cdee9bc21e1486795970ad4f5226f"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#adb1cdee9bc21e1486795970ad4f5226f">More...</a><br/></td></tr>
<tr class="separator:adb1cdee9bc21e1486795970ad4f5226f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a076170589f3fe0c1f306e42a5a3e2962"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a076170589f3fe0c1f306e42a5a3e2962">setWeightB</a> (float weight)=0</td></tr>
<tr class="memdesc:a076170589f3fe0c1f306e42a5a3e2962"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#a076170589f3fe0c1f306e42a5a3e2962">More...</a><br/></td></tr>
<tr class="separator:a076170589f3fe0c1f306e42a5a3e2962"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aefbda82326aa30a385616a609e653097"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aefbda82326aa30a385616a609e653097">setWeightContrast</a> (float weight)=0</td></tr>
<tr class="memdesc:aefbda82326aa30a385616a609e653097"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#aefbda82326aa30a385616a609e653097">More...</a><br/></td></tr>
<tr class="separator:aefbda82326aa30a385616a609e653097"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad4284dee436fef8bbc5153cf01cc69db"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ad4284dee436fef8bbc5153cf01cc69db">setWeightEntropy</a> (float weight)=0</td></tr>
<tr class="memdesc:ad4284dee436fef8bbc5153cf01cc69db"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#ad4284dee436fef8bbc5153cf01cc69db">More...</a><br/></td></tr>
<tr class="separator:ad4284dee436fef8bbc5153cf01cc69db"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:abc703d4edfecaeae94dfdab813fff5f1"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#abc703d4edfecaeae94dfdab813fff5f1">setWeightL</a> (float weight)=0</td></tr>
<tr class="memdesc:abc703d4edfecaeae94dfdab813fff5f1"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#abc703d4edfecaeae94dfdab813fff5f1">More...</a><br/></td></tr>
<tr class="separator:abc703d4edfecaeae94dfdab813fff5f1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0fcdbc44a5a48b4b24e6c182ca7045f7"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a0fcdbc44a5a48b4b24e6c182ca7045f7">setWeights</a> (const std::vector&lt; float &gt; &amp;weights)=0</td></tr>
<tr class="memdesc:a0fcdbc44a5a48b4b24e6c182ca7045f7"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space.  <a href="#a0fcdbc44a5a48b4b24e6c182ca7045f7">More...</a><br/></td></tr>
<tr class="separator:a0fcdbc44a5a48b4b24e6c182ca7045f7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab92c90a1047a839b7946c6711eafb678"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ab92c90a1047a839b7946c6711eafb678">setWeightX</a> (float weight)=0</td></tr>
<tr class="memdesc:ab92c90a1047a839b7946c6711eafb678"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#ab92c90a1047a839b7946c6711eafb678">More...</a><br/></td></tr>
<tr class="separator:ab92c90a1047a839b7946c6711eafb678"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac7a4326f24547f0e21f5b14c0e91d7f7"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ac7a4326f24547f0e21f5b14c0e91d7f7">setWeightY</a> (float weight)=0</td></tr>
<tr class="memdesc:ac7a4326f24547f0e21f5b14c0e91d7f7"><td class="mdescLeft"> </td><td class="mdescRight">Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)  <a href="#ac7a4326f24547f0e21f5b14c0e91d7f7">More...</a><br/></td></tr>
<tr class="separator:ac7a4326f24547f0e21f5b14c0e91d7f7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a424bb76f76544c6f04777d806a033541"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a424bb76f76544c6f04777d806a033541">setWindowRadius</a> (int radius)=0</td></tr>
<tr class="memdesc:a424bb76f76544c6f04777d806a033541"><td class="mdescLeft"> </td><td class="mdescRight">Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).  <a href="#a424bb76f76544c6f04777d806a033541">More...</a><br/></td></tr>
<tr class="separator:a424bb76f76544c6f04777d806a033541"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="inherit_header pub_methods_classcv_1_1Algorithm"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classcv_1_1Algorithm')"><img alt="-" src="../../closed.png"/> Public Member Functions inherited from <a class="el" href="../../d3/d46/classcv_1_1Algorithm.html">cv::Algorithm</a></td></tr>
<tr class="memitem:a827c8b2781ed17574805f373e6054ff1 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a827c8b2781ed17574805f373e6054ff1">Algorithm</a> ()</td></tr>
<tr class="separator:a827c8b2781ed17574805f373e6054ff1 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8ae826127fa0f1f8d10a24841bd376f8 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a8ae826127fa0f1f8d10a24841bd376f8">~Algorithm</a> ()</td></tr>
<tr class="separator:a8ae826127fa0f1f8d10a24841bd376f8 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aec9c965448e4dc851d7cacd3abd84cd1 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#aec9c965448e4dc851d7cacd3abd84cd1">clear</a> ()</td></tr>
<tr class="memdesc:aec9c965448e4dc851d7cacd3abd84cd1 inherit pub_methods_classcv_1_1Algorithm"><td class="mdescLeft"> </td><td class="mdescRight">Clears the algorithm state.  <a href="../../d3/d46/classcv_1_1Algorithm.html#aec9c965448e4dc851d7cacd3abd84cd1">More...</a><br/></td></tr>
<tr class="separator:aec9c965448e4dc851d7cacd3abd84cd1 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab6a18f1825475643e94381697d413972 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">virtual bool </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#ab6a18f1825475643e94381697d413972">empty</a> () const</td></tr>
<tr class="memdesc:ab6a18f1825475643e94381697d413972 inherit pub_methods_classcv_1_1Algorithm"><td class="mdescLeft"> </td><td class="mdescRight">Returns true if the <a class="el" href="../../d3/d46/classcv_1_1Algorithm.html" title="This is a base class for all more or less complex algorithms in OpenCV. ">Algorithm</a> is empty (e.g. in the very beginning or after unsuccessful read.  <a href="../../d3/d46/classcv_1_1Algorithm.html#ab6a18f1825475643e94381697d413972">More...</a><br/></td></tr>
<tr class="separator:ab6a18f1825475643e94381697d413972 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a286fc82744ccab3d248aca44524266a9 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">virtual <a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a286fc82744ccab3d248aca44524266a9">getDefaultName</a> () const</td></tr>
<tr class="separator:a286fc82744ccab3d248aca44524266a9 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aef2ad3f4145bd6e8c3664eb1c4b5e1e6 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#aef2ad3f4145bd6e8c3664eb1c4b5e1e6">read</a> (const <a class="el" href="../../de/dd9/classcv_1_1FileNode.html">FileNode</a> &amp;fn)</td></tr>
<tr class="memdesc:aef2ad3f4145bd6e8c3664eb1c4b5e1e6 inherit pub_methods_classcv_1_1Algorithm"><td class="mdescLeft"> </td><td class="mdescRight">Reads algorithm parameters from a file storage.  <a href="../../d3/d46/classcv_1_1Algorithm.html#aef2ad3f4145bd6e8c3664eb1c4b5e1e6">More...</a><br/></td></tr>
<tr class="separator:aef2ad3f4145bd6e8c3664eb1c4b5e1e6 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0a880744bc4e3f45711444571df47d67 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a0a880744bc4e3f45711444571df47d67">save</a> (const <a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> &amp;filename) const</td></tr>
<tr class="separator:a0a880744bc4e3f45711444571df47d67 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1f8ad7b8add515077367fb9949a174d2 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a1f8ad7b8add515077367fb9949a174d2">write</a> (<a class="el" href="../../da/d56/classcv_1_1FileStorage.html">FileStorage</a> &amp;fs) const</td></tr>
<tr class="memdesc:a1f8ad7b8add515077367fb9949a174d2 inherit pub_methods_classcv_1_1Algorithm"><td class="mdescLeft"> </td><td class="mdescRight">Stores algorithm parameters in a file storage.  <a href="../../d3/d46/classcv_1_1Algorithm.html#a1f8ad7b8add515077367fb9949a174d2">More...</a><br/></td></tr>
<tr class="separator:a1f8ad7b8add515077367fb9949a174d2 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a763a62d1b03042eef7d7fc3ac6c87c79 inherit pub_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a763a62d1b03042eef7d7fc3ac6c87c79">write</a> (const <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt; <a class="el" href="../../da/d56/classcv_1_1FileStorage.html">FileStorage</a> &gt; &amp;fs, const <a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> &amp;name=<a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a>()) const</td></tr>
<tr class="memdesc:a763a62d1b03042eef7d7fc3ac6c87c79 inherit pub_methods_classcv_1_1Algorithm"><td class="mdescLeft"> </td><td class="mdescRight">simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.  <a href="../../d3/d46/classcv_1_1Algorithm.html#a763a62d1b03042eef7d7fc3ac6c87c79">More...</a><br/></td></tr>
<tr class="separator:a763a62d1b03042eef7d7fc3ac6c87c79 inherit pub_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:a9e2ff603a2706f9a64aa00aa7acd6b5a"><td align="right" class="memItemLeft" valign="top">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt; <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html">PCTSignatures</a> &gt; </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a9e2ff603a2706f9a64aa00aa7acd6b5a">create</a> (const int initSampleCount=2000, const int initSeedCount=400, const int pointDistribution=0)</td></tr>
<tr class="memdesc:a9e2ff603a2706f9a64aa00aa7acd6b5a"><td class="mdescLeft"> </td><td class="mdescRight">Creates <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html" title="Class implementing PCT (position-color-texture) signature extraction as described in ...">PCTSignatures</a> algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.  <a href="#a9e2ff603a2706f9a64aa00aa7acd6b5a">More...</a><br/></td></tr>
<tr class="separator:a9e2ff603a2706f9a64aa00aa7acd6b5a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a562a030ed45731adba1a1954270c6825"><td align="right" class="memItemLeft" valign="top">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt; <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html">PCTSignatures</a> &gt; </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a562a030ed45731adba1a1954270c6825">create</a> (const std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; &amp;initSamplingPoints, const int initSeedCount)</td></tr>
<tr class="memdesc:a562a030ed45731adba1a1954270c6825"><td class="mdescLeft"> </td><td class="mdescRight">Creates <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html" title="Class implementing PCT (position-color-texture) signature extraction as described in ...">PCTSignatures</a> algorithm using pre-generated sampling points and number of clusterization seeds. It uses the provided sampling points and generates its own clusterization seed indexes.  <a href="#a562a030ed45731adba1a1954270c6825">More...</a><br/></td></tr>
<tr class="separator:a562a030ed45731adba1a1954270c6825"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a77aa42e4c641f25c33aa9355f8b11352"><td align="right" class="memItemLeft" valign="top">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt; <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html">PCTSignatures</a> &gt; </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a77aa42e4c641f25c33aa9355f8b11352">create</a> (const std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; &amp;initSamplingPoints, const std::vector&lt; int &gt; &amp;initClusterSeedIndexes)</td></tr>
<tr class="memdesc:a77aa42e4c641f25c33aa9355f8b11352"><td class="mdescLeft"> </td><td class="mdescRight">Creates <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html" title="Class implementing PCT (position-color-texture) signature extraction as described in ...">PCTSignatures</a> algorithm using pre-generated sampling points and clusterization seeds indexes.  <a href="#a77aa42e4c641f25c33aa9355f8b11352">More...</a><br/></td></tr>
<tr class="separator:a77aa42e4c641f25c33aa9355f8b11352"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3e206c0847ce9f4474b6084deec1bc84"><td align="right" class="memItemLeft" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#a3e206c0847ce9f4474b6084deec1bc84">drawSignature</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> source, <a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> signature, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> result, float radiusToShorterSideRatio=1.0/8, int borderThickness=1)</td></tr>
<tr class="memdesc:a3e206c0847ce9f4474b6084deec1bc84"><td class="mdescLeft"> </td><td class="mdescRight">Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.  <a href="#a3e206c0847ce9f4474b6084deec1bc84">More...</a><br/></td></tr>
<tr class="separator:a3e206c0847ce9f4474b6084deec1bc84"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ace362f4b93ef4a1c5fd9c419f9a05630"><td align="right" class="memItemLeft" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ace362f4b93ef4a1c5fd9c419f9a05630">generateInitPoints</a> (std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; &amp;initPoints, const int count, int pointDistribution)</td></tr>
<tr class="memdesc:ace362f4b93ef4a1c5fd9c419f9a05630"><td class="mdescLeft"> </td><td class="mdescRight">Generates initial sampling points according to selected point distribution.  <a href="#ace362f4b93ef4a1c5fd9c419f9a05630">More...</a><br/></td></tr>
<tr class="separator:ace362f4b93ef4a1c5fd9c419f9a05630"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="inherit_header pub_static_methods_classcv_1_1Algorithm"><td colspan="2" onclick="javascript:toggleInherit('pub_static_methods_classcv_1_1Algorithm')"><img alt="-" src="../../closed.png"/> Static Public Member Functions inherited from <a class="el" href="../../d3/d46/classcv_1_1Algorithm.html">cv::Algorithm</a></td></tr>
<tr class="memitem:a623841c33b58ea9c4847da04607e067b inherit pub_static_methods_classcv_1_1Algorithm"><td class="memTemplParams" colspan="2">template&lt;typename _Tp &gt; </td></tr>
<tr class="memitem:a623841c33b58ea9c4847da04607e067b inherit pub_static_methods_classcv_1_1Algorithm"><td align="right" class="memTemplItemLeft" valign="top">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt; _Tp &gt; </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a623841c33b58ea9c4847da04607e067b">load</a> (const <a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> &amp;filename, const <a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> &amp;objname=<a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a>())</td></tr>
<tr class="memdesc:a623841c33b58ea9c4847da04607e067b inherit pub_static_methods_classcv_1_1Algorithm"><td class="mdescLeft"> </td><td class="mdescRight">Loads algorithm from the file.  <a href="../../d3/d46/classcv_1_1Algorithm.html#a623841c33b58ea9c4847da04607e067b">More...</a><br/></td></tr>
<tr class="separator:a623841c33b58ea9c4847da04607e067b inherit pub_static_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3ba305a10d02479c13cf7d169c321547 inherit pub_static_methods_classcv_1_1Algorithm"><td class="memTemplParams" colspan="2">template&lt;typename _Tp &gt; </td></tr>
<tr class="memitem:a3ba305a10d02479c13cf7d169c321547 inherit pub_static_methods_classcv_1_1Algorithm"><td align="right" class="memTemplItemLeft" valign="top">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt; _Tp &gt; </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a3ba305a10d02479c13cf7d169c321547">loadFromString</a> (const <a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> &amp;strModel, const <a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> &amp;objname=<a class="el" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a>())</td></tr>
<tr class="memdesc:a3ba305a10d02479c13cf7d169c321547 inherit pub_static_methods_classcv_1_1Algorithm"><td class="mdescLeft"> </td><td class="mdescRight">Loads algorithm from a String.  <a href="../../d3/d46/classcv_1_1Algorithm.html#a3ba305a10d02479c13cf7d169c321547">More...</a><br/></td></tr>
<tr class="separator:a3ba305a10d02479c13cf7d169c321547 inherit pub_static_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad8c591bacb34c485f5b7a250c314fc53 inherit pub_static_methods_classcv_1_1Algorithm"><td class="memTemplParams" colspan="2">template&lt;typename _Tp &gt; </td></tr>
<tr class="memitem:ad8c591bacb34c485f5b7a250c314fc53 inherit pub_static_methods_classcv_1_1Algorithm"><td align="right" class="memTemplItemLeft" valign="top">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt; _Tp &gt; </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#ad8c591bacb34c485f5b7a250c314fc53">read</a> (const <a class="el" href="../../de/dd9/classcv_1_1FileNode.html">FileNode</a> &amp;fn)</td></tr>
<tr class="memdesc:ad8c591bacb34c485f5b7a250c314fc53 inherit pub_static_methods_classcv_1_1Algorithm"><td class="mdescLeft"> </td><td class="mdescRight">Reads algorithm from the file node.  <a href="../../d3/d46/classcv_1_1Algorithm.html#ad8c591bacb34c485f5b7a250c314fc53">More...</a><br/></td></tr>
<tr class="separator:ad8c591bacb34c485f5b7a250c314fc53 inherit pub_static_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a>
Additional Inherited Members</h2></td></tr>
<tr class="inherit_header pro_methods_classcv_1_1Algorithm"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classcv_1_1Algorithm')"><img alt="-" src="../../closed.png"/> Protected Member Functions inherited from <a class="el" href="../../d3/d46/classcv_1_1Algorithm.html">cv::Algorithm</a></td></tr>
<tr class="memitem:a68eeca71617474ad3d4561786f0289d2 inherit pro_methods_classcv_1_1Algorithm"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d46/classcv_1_1Algorithm.html#a68eeca71617474ad3d4561786f0289d2">writeFormat</a> (<a class="el" href="../../da/d56/classcv_1_1FileStorage.html">FileStorage</a> &amp;fs) const</td></tr>
<tr class="separator:a68eeca71617474ad3d4561786f0289d2 inherit pro_methods_classcv_1_1Algorithm"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a id="details" name="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Class implementing PCT (position-color-texture) signature extraction as described in <a class="el" href="../../d0/de3/citelist.html#CITEREF_KrulisLS16">[133]</a>. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image. </p>
<p>A signature is an array of SIGNATURE_DIMENSION-dimensional points. Used dimensions are: weight, x, y position; lab color, contrast, entropy. <a class="el" href="../../d0/de3/citelist.html#CITEREF_KrulisLS16">[133]</a> <a class="el" href="../../d0/de3/citelist.html#CITEREF_BeecksUS10">[17]</a> </p>
</div><h2 class="groupheader">Member Enumeration Documentation</h2>
<a id="afbfb1e721fa42ab53fe6cd733a51af9c"></a>
<h2 class="memtitle"><span class="permalink"><a href="#afbfb1e721fa42ab53fe6cd733a51af9c">◆ </a></span>DistanceFunction</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">enum <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#afbfb1e721fa42ab53fe6cd733a51af9c">cv::xfeatures2d::PCTSignatures::DistanceFunction</a></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Lp distance function selector. </p>
<table class="fieldtable">
<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="afbfb1e721fa42ab53fe6cd733a51af9ca329bc6c9f8b2f66e0074b5b7d53c6c44"></a>L0_25 </td><td class="fielddoc"></td></tr>
<tr><td class="fieldname"><a id="afbfb1e721fa42ab53fe6cd733a51af9ca6f767af8fdecce184366a58e6446f0f6"></a>L0_5 </td><td class="fielddoc"></td></tr>
<tr><td class="fieldname"><a id="afbfb1e721fa42ab53fe6cd733a51af9caa4d7e3bd3f4f28c1a9460b5b0314395a"></a>L1 </td><td class="fielddoc"></td></tr>
<tr><td class="fieldname"><a id="afbfb1e721fa42ab53fe6cd733a51af9cac9cff6959282d25cd4c9cc3008a17dcc"></a>L2 </td><td class="fielddoc"></td></tr>
<tr><td class="fieldname"><a id="afbfb1e721fa42ab53fe6cd733a51af9ca512d1f81e9e6f513bf7c338947a430cd"></a>L2SQUARED </td><td class="fielddoc"></td></tr>
<tr><td class="fieldname"><a id="afbfb1e721fa42ab53fe6cd733a51af9ca9b61ece212bb491a39009290c1b47b9b"></a>L5 </td><td class="fielddoc"></td></tr>
<tr><td class="fieldname"><a id="afbfb1e721fa42ab53fe6cd733a51af9ca66115018ae41e0ef9a0bacd83fbbccfc"></a>L_INFINITY </td><td class="fielddoc"></td></tr>
</table>
</div>
</div>
<a id="aa2a27f17a1a30fad4c54c248b77777e0"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa2a27f17a1a30fad4c54c248b77777e0">◆ </a></span>PointDistribution</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">enum <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#aa2a27f17a1a30fad4c54c248b77777e0">cv::xfeatures2d::PCTSignatures::PointDistribution</a></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Point distributions supported by random point generator. </p>
<table class="fieldtable">
<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="aa2a27f17a1a30fad4c54c248b77777e0a5fb71e96bee38313ae3600a6d8204248"></a>UNIFORM </td><td class="fielddoc"><p>Generate numbers uniformly. </p>
</td></tr>
<tr><td class="fieldname"><a id="aa2a27f17a1a30fad4c54c248b77777e0a9904ebe58740486ab511151a7b9067fd"></a>REGULAR </td><td class="fielddoc"><p>Generate points in a regular grid. </p>
</td></tr>
<tr><td class="fieldname"><a id="aa2a27f17a1a30fad4c54c248b77777e0af115eca6e3361cd5ec6cb1887f0aabd3"></a>NORMAL </td><td class="fielddoc"><p>Generate points with normal (gaussian) distribution. </p>
</td></tr>
</table>
</div>
</div>
<a id="ac72268153bf12925f601c4defe7d5e50"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ac72268153bf12925f601c4defe7d5e50">◆ </a></span>SimilarityFunction</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">enum <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html#ac72268153bf12925f601c4defe7d5e50">cv::xfeatures2d::PCTSignatures::SimilarityFunction</a></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Similarity function selector. </p>
<dl class="section see"><dt>See also</dt><dd>Christian Beecks, Merih Seran Uysal, Thomas Seidl. Signature quadratic form distance. In Proceedings of the ACM International Conference on Image and Video Retrieval, pages 438-445. ACM, 2010. <a class="el" href="../../d0/de3/citelist.html#CITEREF_BeecksUS10">[17]</a> </dd></dl>
<dl class="section note"><dt>Note</dt><dd>For selected distance function: <p class="formulaDsp">
\[ d(c_i, c_j) \]
</p>
 and parameter: <p class="formulaDsp">
\[ \alpha \]
</p>
 </dd></dl>
<table class="fieldtable">
<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="ac72268153bf12925f601c4defe7d5e50ae4c8b209454fa9578ddb3af45da854c2"></a>MINUS </td><td class="fielddoc"><p class="formulaDsp">
\[ -d(c_i, c_j) \]
</p>
 </td></tr>
<tr><td class="fieldname"><a id="ac72268153bf12925f601c4defe7d5e50a90fc80ce009b279cde69707d90988268"></a>GAUSSIAN </td><td class="fielddoc"><p class="formulaDsp">
\[ e^{ -\alpha * d^2(c_i, c_j)} \]
</p>
 </td></tr>
<tr><td class="fieldname"><a id="ac72268153bf12925f601c4defe7d5e50a999778061578f114ebf7ec41b3f5d858"></a>HEURISTIC </td><td class="fielddoc"><p class="formulaDsp">
\[ \frac{1}{\alpha + d(c_i, c_j)} \]
</p>
 </td></tr>
</table>
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
<a id="a23afba0a61f447c839fbf0134af817eb"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a23afba0a61f447c839fbf0134af817eb">◆ </a></span>computeSignature()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::computeSignature </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>image</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>signature</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>signature</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.computeSignature(</td><td class="paramname">image[, signature]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Computes signature of given image. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">image</td><td>Input image of CV_8U type. </td></tr>
    <tr><td class="paramname">signature</td><td>Output computed signature. </td></tr>
  </table>
  </dd>
</dl>
</div>
</div>
<a id="a7e64cf57c74009277db78e99d14af4d2"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a7e64cf57c74009277db78e99d14af4d2">◆ </a></span>computeSignatures()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::computeSignatures </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &gt; &amp; </td>
          <td class="paramname"><em>images</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &gt; &amp; </td>
          <td class="paramname"><em>signatures</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.computeSignatures(</td><td class="paramname">images, signatures</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Computes signatures for multiple images in parallel. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">images</td><td>Vector of input images of CV_8U type. </td></tr>
    <tr><td class="paramname">signatures</td><td>Vector of computed signatures. </td></tr>
  </table>
  </dd>
</dl>
</div>
</div>
<a id="a9e2ff603a2706f9a64aa00aa7acd6b5a"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a9e2ff603a2706f9a64aa00aa7acd6b5a">◆ </a></span>create() <span class="overload">[1/3]</span></h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt;<a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html">PCTSignatures</a>&gt; cv::xfeatures2d::PCTSignatures::create </td>
          <td>(</td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>initSampleCount</em> = <code>2000</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>initSeedCount</em> = <code>400</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>pointDistribution</em> = <code>0</code> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">static</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">[, initSampleCount[, initSeedCount[, pointDistribution]]]</td><td>)</td></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">initSamplingPoints, initSeedCount</td><td>)</td></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">initSamplingPoints, initClusterSeedIndexes</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Creates <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html" title="Class implementing PCT (position-color-texture) signature extraction as described in ...">PCTSignatures</a> algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">initSampleCount</td><td>Number of points used for image sampling. </td></tr>
    <tr><td class="paramname">initSeedCount</td><td>Number of initial clusterization seeds. Must be lower or equal to initSampleCount </td></tr>
    <tr><td class="paramname">pointDistribution</td><td>Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Created algorithm. </dd></dl>
</div>
</div>
<a id="a562a030ed45731adba1a1954270c6825"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a562a030ed45731adba1a1954270c6825">◆ </a></span>create() <span class="overload">[2/3]</span></h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt;<a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html">PCTSignatures</a>&gt; cv::xfeatures2d::PCTSignatures::create </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; &amp; </td>
          <td class="paramname"><em>initSamplingPoints</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>initSeedCount</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">static</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">[, initSampleCount[, initSeedCount[, pointDistribution]]]</td><td>)</td></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">initSamplingPoints, initSeedCount</td><td>)</td></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">initSamplingPoints, initClusterSeedIndexes</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Creates <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html" title="Class implementing PCT (position-color-texture) signature extraction as described in ...">PCTSignatures</a> algorithm using pre-generated sampling points and number of clusterization seeds. It uses the provided sampling points and generates its own clusterization seed indexes. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">initSamplingPoints</td><td>Sampling points used in image sampling. </td></tr>
    <tr><td class="paramname">initSeedCount</td><td>Number of initial clusterization seeds. Must be lower or equal to <a class="el" href="../../df/d5b/namespacecv_1_1gapi_1_1streaming.html#a0a915e69f4cc8284293e40fc9ffbf157" title="Gets dimensions from Mat. ">initSamplingPoints.size()</a>. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Created algorithm. </dd></dl>
</div>
</div>
<a id="a77aa42e4c641f25c33aa9355f8b11352"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a77aa42e4c641f25c33aa9355f8b11352">◆ </a></span>create() <span class="overload">[3/3]</span></h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">static <a class="el" href="../../dc/d84/group__core__basic.html#ga6395ca871a678020c4a31fadf7e8cc63">Ptr</a>&lt;<a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html">PCTSignatures</a>&gt; cv::xfeatures2d::PCTSignatures::create </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; &amp; </td>
          <td class="paramname"><em>initSamplingPoints</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const std::vector&lt; int &gt; &amp; </td>
          <td class="paramname"><em>initClusterSeedIndexes</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">static</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">[, initSampleCount[, initSeedCount[, pointDistribution]]]</td><td>)</td></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">initSamplingPoints, initSeedCount</td><td>)</td></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_create(</td><td class="paramname">initSamplingPoints, initClusterSeedIndexes</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Creates <a class="el" href="../../d0/d74/classcv_1_1xfeatures2d_1_1PCTSignatures.html" title="Class implementing PCT (position-color-texture) signature extraction as described in ...">PCTSignatures</a> algorithm using pre-generated sampling points and clusterization seeds indexes. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">initSamplingPoints</td><td>Sampling points used in image sampling. </td></tr>
    <tr><td class="paramname">initClusterSeedIndexes</td><td>Indexes of initial clusterization seeds. Its size must be lower or equal to <a class="el" href="../../df/d5b/namespacecv_1_1gapi_1_1streaming.html#a0a915e69f4cc8284293e40fc9ffbf157" title="Gets dimensions from Mat. ">initSamplingPoints.size()</a>. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Created algorithm. </dd></dl>
</div>
</div>
<a id="a3e206c0847ce9f4474b6084deec1bc84"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a3e206c0847ce9f4474b6084deec1bc84">◆ </a></span>drawSignature()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">static void cv::xfeatures2d::PCTSignatures::drawSignature </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>source</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>signature</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>result</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>radiusToShorterSideRatio</em> = <code>1.0/8</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>borderThickness</em> = <code>1</code> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">static</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>result</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_drawSignature(</td><td class="paramname">source, signature[, result[, radiusToShorterSideRatio[, borderThickness]]]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">source</td><td>Source image. </td></tr>
    <tr><td class="paramname">signature</td><td>Image signature. </td></tr>
    <tr><td class="paramname">result</td><td>Output result. </td></tr>
    <tr><td class="paramname">radiusToShorterSideRatio</td><td>Determines maximal radius of signature in the output image. </td></tr>
    <tr><td class="paramname">borderThickness</td><td>Border thickness of the visualized signature. </td></tr>
  </table>
  </dd>
</dl>
</div>
</div>
<a id="ace362f4b93ef4a1c5fd9c419f9a05630"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ace362f4b93ef4a1c5fd9c419f9a05630">◆ </a></span>generateInitPoints()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">static void cv::xfeatures2d::PCTSignatures::generateInitPoints </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; &amp; </td>
          <td class="paramname"><em>initPoints</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>count</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>pointDistribution</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">static</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d.PCTSignatures_generateInitPoints(</td><td class="paramname">initPoints, count, pointDistribution</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Generates initial sampling points according to selected point distribution. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">initPoints</td><td>Output vector where the generated points will be saved. </td></tr>
    <tr><td class="paramname">count</td><td>Number of points to generate. </td></tr>
    <tr><td class="paramname">pointDistribution</td><td>Point distribution selector. Available: UNIFORM, REGULAR, NORMAL. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>Generated coordinates are in range [0..1) </dd></dl>
</div>
</div>
<a id="a80b1bcbb2621d01887f0ce0cf2153d27"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a80b1bcbb2621d01887f0ce0cf2153d27">◆ </a></span>getClusterMinSize()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int cv::xfeatures2d::PCTSignatures::getClusterMinSize </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getClusterMinSize(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned. </p>
</div>
</div>
<a id="a00e5d015965077e63a3d026315d99fb7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a00e5d015965077e63a3d026315d99fb7">◆ </a></span>getDistanceFunction()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int cv::xfeatures2d::PCTSignatures::getDistanceFunction </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getDistanceFunction(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Distance function selector used for measuring distance between two points in k-means. </p>
</div>
</div>
<a id="aa0de847070a8f7557feed32220f6d097"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa0de847070a8f7557feed32220f6d097">◆ </a></span>getDropThreshold()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getDropThreshold </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getDropThreshold(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Remove centroids in k-means whose weight is lesser or equal to given threshold. </p>
</div>
</div>
<a id="a147d4d9af6c8f0fac8509a4f62d20306"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a147d4d9af6c8f0fac8509a4f62d20306">◆ </a></span>getGrayscaleBits()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int cv::xfeatures2d::PCTSignatures::getGrayscaleBits </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getGrayscaleBits(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values. </p>
</div>
</div>
<a id="a2d73ca7a104084525f33f76b8f062da7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a2d73ca7a104084525f33f76b8f062da7">◆ </a></span>getInitSeedCount()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int cv::xfeatures2d::PCTSignatures::getInitSeedCount </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getInitSeedCount(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Number of initial seeds (initial number of clusters) for the k-means algorithm. </p>
</div>
</div>
<a id="a05099a75bbbe848a83aba350686ce20d"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a05099a75bbbe848a83aba350686ce20d">◆ </a></span>getInitSeedIndexes()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual std::vector&lt;int&gt; cv::xfeatures2d::PCTSignatures::getInitSeedIndexes </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getInitSeedIndexes(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Initial seeds (initial number of clusters) for the k-means algorithm. </p>
</div>
</div>
<a id="aa09a2face423a7042bdec81e6526190b"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa09a2face423a7042bdec81e6526190b">◆ </a></span>getIterationCount()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int cv::xfeatures2d::PCTSignatures::getIterationCount </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getIterationCount(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters). </p>
</div>
</div>
<a id="ab140533fa85a158ebbbed43d9e03973b"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ab140533fa85a158ebbbed43d9e03973b">◆ </a></span>getJoiningDistance()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getJoiningDistance </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getJoiningDistance(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned. </p>
</div>
</div>
<a id="a68d214927159651492e648e5adde1345"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a68d214927159651492e648e5adde1345">◆ </a></span>getMaxClustersCount()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int cv::xfeatures2d::PCTSignatures::getMaxClustersCount </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getMaxClustersCount(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped. </p>
</div>
</div>
<a id="a16c500a45db6acdf2d5989d2f0563bfc"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a16c500a45db6acdf2d5989d2f0563bfc">◆ </a></span>getSampleCount()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int cv::xfeatures2d::PCTSignatures::getSampleCount </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getSampleCount(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Number of initial samples taken from the image. </p>
</div>
</div>
<a id="a3587514c31b90a137b87b437f373cbdd"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a3587514c31b90a137b87b437f373cbdd">◆ </a></span>getSamplingPoints()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual std::vector&lt;<a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a>&gt; cv::xfeatures2d::PCTSignatures::getSamplingPoints </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getSamplingPoints(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Initial samples taken from the image. These sampled features become the input for clustering. </p>
</div>
</div>
<a id="a92ea655d80cc73ebc4d9c4528a2e13aa"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a92ea655d80cc73ebc4d9c4528a2e13aa">◆ </a></span>getWeightA()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getWeightA </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getWeightA(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="a2c05236f9cb324ea0b457cc4aad73002"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a2c05236f9cb324ea0b457cc4aad73002">◆ </a></span>getWeightB()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getWeightB </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getWeightB(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="a8ba46a2ee95336bd50ce38c5f7bfe895"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a8ba46a2ee95336bd50ce38c5f7bfe895">◆ </a></span>getWeightContrast()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getWeightContrast </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getWeightContrast(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="a2c13123bc1ca583006de657ab640e921"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a2c13123bc1ca583006de657ab640e921">◆ </a></span>getWeightEntropy()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getWeightEntropy </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getWeightEntropy(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="a66b254681f41fe131ce9f9355de977ff"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a66b254681f41fe131ce9f9355de977ff">◆ </a></span>getWeightL()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getWeightL </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getWeightL(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="a1b3c726c6d7485a9f20ab9ecdecb79a1"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a1b3c726c6d7485a9f20ab9ecdecb79a1">◆ </a></span>getWeightX()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getWeightX </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getWeightX(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="afca4d096cd24693c007c8197f2242591"></a>
<h2 class="memtitle"><span class="permalink"><a href="#afca4d096cd24693c007c8197f2242591">◆ </a></span>getWeightY()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual float cv::xfeatures2d::PCTSignatures::getWeightY </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getWeightY(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="ae382e1d59ca2eaf13c482c6cffc50749"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ae382e1d59ca2eaf13c482c6cffc50749">◆ </a></span>getWindowRadius()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int cv::xfeatures2d::PCTSignatures::getWindowRadius </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>retval</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.getWindowRadius(</td><td class="paramname"></td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample). </p>
</div>
</div>
<a id="a9b1788e47f815b0bdba559062e1be914"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a9b1788e47f815b0bdba559062e1be914">◆ </a></span>setClusterMinSize()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setClusterMinSize </td>
          <td>(</td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>clusterMinSize</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setClusterMinSize(</td><td class="paramname">clusterMinSize</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned. </p>
</div>
</div>
<a id="a828b6802c9960647a128b966f7a79045"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a828b6802c9960647a128b966f7a79045">◆ </a></span>setDistanceFunction()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setDistanceFunction </td>
          <td>(</td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>distanceFunction</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setDistanceFunction(</td><td class="paramname">distanceFunction</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Distance function selector used for measuring distance between two points in k-means. Available: L0_25, L0_5, <a class="el" href="../../d4/d7f/structcv_1_1L1.html">L1</a>, <a class="el" href="../../dc/daa/structcv_1_1L2.html">L2</a>, L2SQUARED, L5, L_INFINITY. </p>
</div>
</div>
<a id="a48a8b9dcd84024b8cbdcd6d9eae220b7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a48a8b9dcd84024b8cbdcd6d9eae220b7">◆ </a></span>setDropThreshold()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setDropThreshold </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>dropThreshold</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setDropThreshold(</td><td class="paramname">dropThreshold</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Remove centroids in k-means whose weight is lesser or equal to given threshold. </p>
</div>
</div>
<a id="a1d9a4dd5b5b91d8e9f8195c7e496b9c3"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a1d9a4dd5b5b91d8e9f8195c7e496b9c3">◆ </a></span>setGrayscaleBits()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setGrayscaleBits </td>
          <td>(</td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>grayscaleBits</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setGrayscaleBits(</td><td class="paramname">grayscaleBits</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values. </p>
</div>
</div>
<a id="a0aa3ed847a3bd993e34a9bdb294212f5"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a0aa3ed847a3bd993e34a9bdb294212f5">◆ </a></span>setInitSeedIndexes()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setInitSeedIndexes </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; int &gt; </td>
          <td class="paramname"><em>initSeedIndexes</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setInitSeedIndexes(</td><td class="paramname">initSeedIndexes</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Initial seed indexes for the k-means algorithm. </p>
</div>
</div>
<a id="aa2f1491f6cc4f99feed74a55c84a6950"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa2f1491f6cc4f99feed74a55c84a6950">◆ </a></span>setIterationCount()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setIterationCount </td>
          <td>(</td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>iterationCount</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setIterationCount(</td><td class="paramname">iterationCount</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters). </p>
</div>
</div>
<a id="a3b34e63ce62c9759d7a9747ba6549a78"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a3b34e63ce62c9759d7a9747ba6549a78">◆ </a></span>setJoiningDistance()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setJoiningDistance </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>joiningDistance</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setJoiningDistance(</td><td class="paramname">joiningDistance</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned. </p>
</div>
</div>
<a id="a4450e6f3e2676caaf4c78c9da7e6af6f"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a4450e6f3e2676caaf4c78c9da7e6af6f">◆ </a></span>setMaxClustersCount()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setMaxClustersCount </td>
          <td>(</td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>maxClustersCount</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setMaxClustersCount(</td><td class="paramname">maxClustersCount</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped. </p>
</div>
</div>
<a id="a04adf50e23489c41975a6b7524c189d4"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a04adf50e23489c41975a6b7524c189d4">◆ </a></span>setSamplingPoints()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setSamplingPoints </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; <a class="el" href="../../dc/d84/group__core__basic.html#ga7d080aa40de011e4410bca63385ffe2a">Point2f</a> &gt; </td>
          <td class="paramname"><em>samplingPoints</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setSamplingPoints(</td><td class="paramname">samplingPoints</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Sets sampling points used to sample the input image. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">samplingPoints</td><td>Vector of sampling points in range [0..1) </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>Number of sampling points must be greater or equal to clusterization seed count. </dd></dl>
</div>
</div>
<a id="a326cff067288459c6fefa0f3fe313a14"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a326cff067288459c6fefa0f3fe313a14">◆ </a></span>setTranslation()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setTranslation </td>
          <td>(</td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>idx</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>value</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setTranslation(</td><td class="paramname">idx, value</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Translations of the individual axes of the feature space. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">idx</td><td>ID of the translation </td></tr>
    <tr><td class="paramname">value</td><td>Value of the translation </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7; </dd></dl>
</div>
</div>
<a id="a48a29104ac014ba9adc426ec67568ed7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a48a29104ac014ba9adc426ec67568ed7">◆ </a></span>setTranslations()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setTranslations </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; float &gt; &amp; </td>
          <td class="paramname"><em>translations</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setTranslations(</td><td class="paramname">translations</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Translations of the individual axes of the feature space. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">translations</td><td>Values of all translations. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7; </dd></dl>
</div>
</div>
<a id="a644513e799afdefdb0e37a1bb4abc2d7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a644513e799afdefdb0e37a1bb4abc2d7">◆ </a></span>setWeight()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeight </td>
          <td>(</td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>idx</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>value</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeight(</td><td class="paramname">idx, value</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">idx</td><td>ID of the weight </td></tr>
    <tr><td class="paramname">value</td><td>Value of the weight </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7; </dd></dl>
</div>
</div>
<a id="adb1cdee9bc21e1486795970ad4f5226f"></a>
<h2 class="memtitle"><span class="permalink"><a href="#adb1cdee9bc21e1486795970ad4f5226f">◆ </a></span>setWeightA()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeightA </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>weight</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeightA(</td><td class="paramname">weight</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="a076170589f3fe0c1f306e42a5a3e2962"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a076170589f3fe0c1f306e42a5a3e2962">◆ </a></span>setWeightB()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeightB </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>weight</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeightB(</td><td class="paramname">weight</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="aefbda82326aa30a385616a609e653097"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aefbda82326aa30a385616a609e653097">◆ </a></span>setWeightContrast()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeightContrast </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>weight</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeightContrast(</td><td class="paramname">weight</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="ad4284dee436fef8bbc5153cf01cc69db"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ad4284dee436fef8bbc5153cf01cc69db">◆ </a></span>setWeightEntropy()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeightEntropy </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>weight</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeightEntropy(</td><td class="paramname">weight</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="abc703d4edfecaeae94dfdab813fff5f1"></a>
<h2 class="memtitle"><span class="permalink"><a href="#abc703d4edfecaeae94dfdab813fff5f1">◆ </a></span>setWeightL()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeightL </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>weight</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeightL(</td><td class="paramname">weight</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="a0fcdbc44a5a48b4b24e6c182ca7045f7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a0fcdbc44a5a48b4b24e6c182ca7045f7">◆ </a></span>setWeights()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeights </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; float &gt; &amp; </td>
          <td class="paramname"><em>weights</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeights(</td><td class="paramname">weights</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">weights</td><td>Values of all weights. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>Note</dt><dd>WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7; </dd></dl>
</div>
</div>
<a id="ab92c90a1047a839b7946c6711eafb678"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ab92c90a1047a839b7946c6711eafb678">◆ </a></span>setWeightX()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeightX </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>weight</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeightX(</td><td class="paramname">weight</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="ac7a4326f24547f0e21f5b14c0e91d7f7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ac7a4326f24547f0e21f5b14c0e91d7f7">◆ </a></span>setWeightY()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWeightY </td>
          <td>(</td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>weight</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWeightY(</td><td class="paramname">weight</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) </p>
</div>
</div>
<a id="a424bb76f76544c6f04777d806a033541"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a424bb76f76544c6f04777d806a033541">◆ </a></span>setWindowRadius()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void cv::xfeatures2d::PCTSignatures::setWindowRadius </td>
          <td>(</td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>radius</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.xfeatures2d_PCTSignatures.setWindowRadius(</td><td class="paramname">radius</td><td>)</td></tr></table>
</div><div class="memdoc">
<p>Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample). </p>
</div>
</div>
<hr/>The documentation for this class was generated from the following file:<ul>
<li>opencv2/<a class="el" href="../../dc/daa/xfeatures2d_8hpp.html">xfeatures2d.hpp</a></li>
</ul>
</div><!-- contents -->
<!-- HTML footer for doxygen 1.8.6-->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
Generated on Fri Apr 2 2021 11:36:49 for OpenCV by  <a href="http://www.doxygen.org/index.html">
<img alt="doxygen" class="footer" src="../../doxygen.png"/>
</a> 1.8.13
</small></address>
<script type="text/javascript">
//<![CDATA[
addTutorialsButtons();
//]]>
</script>
</body>
</html>
